"Classroom data are like teddy bears and real data are like a grizzley bear with salmon blood dripping out its mouth."
Jenny Bryan                  Â
"Up to 80% of data analysis is spent on the process of cleaning and preparing data."
cf. Wickham, 2014 and Dasu & Johnson, 2003 Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â
"Cannot emphasize enough how much time you save by putting analysis efforts into tidying data first."
Hilary Parker                  Â
1. One variable per column
2. One observation per row
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## 2 1192601 14 2 1999 11 female 6 0
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## [ reached getOption("max.print") -- omitted 92337 rows ]
Is this data tidy?
## State X1980 X1990 X2000 X2005 X2006 ## 1 United States ......... 2725285 2320337 2553844 2799250 2815544 ## 2 Alabama .................. 44894 40485 37819 37453 37918 ## 3 Alaska ................... 5343 5386 6615 6909 7361 ## 4 Arizona .................. 28416 32103 38304 59498 54091 ## 5 Arkansas ................. 29577 26475 27335 26621 28790 ## 6 California ............... 242172 236291 309866 355217 343515 ## 7 Colorado ................. 35897 32967 38924 44532 44424 ## 8 Connecticut .............. 38369 27878 31562 35515 36222 ## 9 Delaware ................. 7349 5550 6108 6934 7275 ## 10 Florida .................. 88755 88934 106708 133318 134686 ## 11 Georgia .................. 62963 56605 62563 70834 73498 ## 12 Hawaii ................... 11472 10325 10437 10813 10922 ## 13 Idaho .................... 12679 11971 16170 15768 16096 ## 14 Illinois ................. 136795 108119 111835 123615 126817 ## 15 Indiana .................. 73381 60012 57012 55444 57920 ## 16 Iowa ..................... 42635 31796 33926 33547 33693 ## 17 Kansas ................... 29397 25367 29102 30355 29818 ## 18 Kentucky ................. 41714 38005 36830 38399 38449 ## 19 Louisiana ................ 46199 36053 38430 36009 33275 ## 20 Maine .................... 15554 13839 12211 13077 12950 ## 21 Maryland ................. 54050 41566 47849 54170 55536 ## 22 Massachusetts ............ 74831 55941 52950 59665 61272 ## 23 Michigan ................. 124372 93807 97679 101582 102582 ## 24 Minnesota ................ 64166 49087 57372 58391 58898 ## 25 Mississippi .............. 28083 25182 24232 23523 23848 ## 26 Missouri ................. 60359 48957 52848 57841 58417 ## 27 Montana .................. 11634 9370 10903 10335 10283 ## 28 Nebraska ................. 21411 17664 20149 19940 19764 ## 29 Nevada ................... 9069 9477 14551 15740 16455 ## 30 New Hampshire ............ 11552 10766 11829 13775 13988 ## 31 New Jersey ............... 93168 69824 74420 86502 90049 ## 32 New Mexico ............... 17915 14884 18031 17353 17822 ## 33 New York ................. 198465 143318 141731 153203 161817 ## 34 North Carolina ........... 69395 64782 62140 75010 76710 ## 35 North Dakota ............. 9924 7690 8606 7555 7192 ## 36 Ohio ..................... 143503 114513 111668 116702 117356 ## 37 Oklahoma ................. 38875 35606 37646 36227 36497 ## 38 Oregon ................... 28729 25473 30151 32602 32394 ## 39 Pennsylvania ............. 144645 110527 113959 124758 127830 ## 40 Rhode Island ............. 10719 7825 8477 9881 10108 ## 41 South Carolina ........... 38347 32483 31617 33439 34970 ## 42 South Dakota ............. 10385 7650 9278 8585 8589 ## 43 Tennessee ................ 50648 46094 41568 47967 50880 ## 44 Texas .................... 171665 172480 212925 239717 240485 ## 45 Utah ..................... 19886 21196 32501 30253 29050 ## 46 Vermont .................. 6424 6127 6675 7152 6779 ## 47 Virginia ................. 67126 60605 65596 73667 69597 ## 48 Washington ............... 50046 45941 57597 61094 60213 ## 49 West Virginia ............ 23580 21854 19437 17137 16763 ## 50 Wisconsin ................ 67743 52038 58545 63229 63003 ## 51 Wyoming .................. 6161 5823 6462 5616 5527 ## 52 NA NA NA NA NA ## X2007 X2008 X2009 X2010 X2011 X2012 X2013 X2014 X ## 1 2893045 3001337 3039015 3128022 3121630 3103680 3110150 3070440 NA ## 2 38912 41346 42082 43166 44210 44030 43050 42560 NA ## 3 7666 7855 8008 8245 7750 7970 7230 7210 NA ## 4 55954 61667 62374 61145 65010 64910 64090 63380 NA ## 5 27166 28725 28057 28276 28110 28210 28210 28540 NA ## 6 356641 374561 372310 404987 414180 415600 415370 409660 NA ## 7 45628 46082 47459 49321 51000 51430 52150 52110 NA ## 8 37541 38419 34968 34495 36910 36290 35820 35200 NA ## 9 7205 7388 7839 8133 8220 8320 8140 7950 NA ## 10 142284 149046 153461 156130 160570 157050 164800 163860 NA ## 11 77829 83505 88003 91561 93410 91780 92890 93770 NA ## 12 11063 11613 11508 10998 11090 11360 11000 10900 NA ## 13 16242 16567 16807 17793 17440 17500 17330 17620 NA ## 14 130220 135143 131670 139035 136400 134660 141290 135860 NA ## 15 59887 61901 63663 64551 65750 65090 65670 66570 NA ## 16 34127 34573 33926 34462 33370 32820 32150 31960 NA ## 17 30139 30737 30368 31642 31120 30470 30790 30220 NA ## 18 39099 39339 41851 42664 41540 41750 41700 40130 NA ## 19 34274 34401 35622 36573 34330 34920 35510 36510 NA ## 20 13151 14350 14093 14069 14040 13650 13250 12850 NA ## 21 57564 59171 58304 59078 58010 57770 57220 55910 NA ## 22 63903 65197 65258 64462 63320 63090 63370 62250 NA ## 23 111838 115183 112742 110682 107080 105580 102770 101970 NA ## 24 59497 60409 59729 59667 58890 57240 56690 55060 NA ## 25 24186 24795 24505 25478 26160 25670 26020 25200 NA ## 26 60275 61717 62969 63994 62170 60760 60190 59720 NA ## 27 10122 10396 10077 10075 9700 9600 9260 9350 NA ## 28 19873 20035 19501 19370 19230 19320 19450 19470 NA ## 29 17149 18815 19904 20956 23460 24360 24350 23820 NA ## 30 14452 14982 14757 15034 14330 14230 14160 13770 NA ## 31 93013 94994 95085 96225 94280 91100 90870 88030 NA ## 32 16131 18264 17931 18595 18900 18980 18470 18130 NA ## 33 168333 176310 180917 183826 185630 184900 184490 176890 NA ## 34 76031 83307 86712 88704 86580 88940 89120 89170 NA ## 35 7159 6999 7232 7155 7060 6840 6830 6720 NA ## 36 117658 120758 122203 123437 110360 109370 106100 103810 NA ## 37 37100 37630 37219 38503 37830 37660 37520 37310 NA ## 38 33446 34949 35138 34671 34690 34460 34010 34160 NA ## 39 128603 130298 130658 131182 130950 127790 123540 119990 NA ## 40 10384 10347 10028 9908 9730 9820 9810 9430 NA ## 41 35108 35303 39114 40438 40050 40050 39830 39420 NA ## 42 8346 8582 8123 8162 8460 8090 8030 8040 NA ## 43 54502 57486 60368 62408 62880 63080 62670 61490 NA ## 44 241193 252121 264275 280894 282670 282960 292560 295600 NA ## 45 28276 28167 30463 31481 30850 31620 31990 32350 NA ## 46 7317 7392 7209 7199 6850 6670 6480 6140 NA ## 47 73997 77369 79651 81511 82080 82150 82080 81080 NA ## 48 62801 61625 62764 66046 65830 66140 66350 65080 NA ## 49 17407 17489 17690 17651 17260 17320 17100 16710 NA ## 50 63968 65183 65410 64687 63020 61710 59890 59140 NA ## 51 5441 5494 5493 5695 5600 5550 5440 5390 NA ## 52 NA NA NA NA NA NA NA NA NA
Is this data tidy?
## Year White_unemployment Black_unemployment White_hs Black_hs ## 1 1972 5.1 10.400000 60.4 36.6 ## 2 1973 4.3 9.425000 61.9 39.2 ## 3 1974 5.1 10.541667 63.3 40.8 ## 4 1975 7.8 14.808333 64.5 42.5 ## 5 1976 7.0 13.950000 66.1 43.8 ## 6 1977 6.2 14.033333 67.0 45.5 ## 7 1978 5.2 12.741667 67.9 47.6 ## 8 1979 5.1 12.341667 69.7 49.4 ## 9 1980 6.3 14.291667 70.5 51.2 ## 10 1981 6.7 15.625000 71.6 52.9 ## 11 1982 8.6 18.908333 72.8 54.9 ## 12 1983 8.4 19.500000 73.8 56.8 ## 13 1984 6.5 15.925000 75.0 58.5 ## 14 1985 6.2 15.091667 75.5 59.8 ## 15 1986 6.0 14.558333 76.2 62.3 ## 16 1987 5.3 12.966667 77.0 63.4 ## 17 1988 4.7 11.708333 77.7 63.5 ## 18 1989 4.5 11.466667 78.4 64.6 ## 19 1990 4.8 11.408333 79.1 66.2 ## 20 1991 6.1 12.491667 79.9 66.7 ## 21 1992 6.6 14.200000 80.9 67.7 ## 22 1993 6.1 12.950000 81.5 70.4 ## 23 1994 5.3 11.491667 82.0 72.9 ## 24 1995 4.9 10.375000 83.0 73.8 ## 25 1996 4.7 10.508333 82.8 74.3 ## 26 1997 4.2 10.058333 83.0 74.9 ## 27 1998 3.9 8.916667 83.7 76.0 ## 28 1999 3.7 8.000000 84.3 77.0 ## 29 2000 3.5 7.591667 84.9 78.5 ## 30 2001 4.2 8.650000 84.8 78.8 ## 31 2002 5.1 10.241667 84.8 78.7 ## 32 2003 5.3 10.783333 85.1 80.0 ## 33 2004 4.8 10.375000 85.8 80.6 ## 34 2005 4.4 9.983333 85.8 81.1 ## 35 2006 4.0 8.950000 86.1 80.7 ## 36 2007 4.1 8.258333 86.2 82.3 ## 37 2008 5.2 10.100000 87.1 83.0 ## 38 2009 8.5 14.783333 87.1 84.1 ## 39 2010 8.7 15.958333 87.6 84.2 ## 40 2011 7.9 15.825000 88.1 84.5 ## 41 2012 7.2 13.808333 88.1 85.0 ## 42 2013 6.5 13.058333 88.6 85.1 ## 43 2014 5.3 11.341667 88.8 85.8 ## white_college black_college ## 1 12.6 5.1 ## 2 13.1 6.0 ## 3 14.0 5.5 ## 4 14.5 6.4 ## 5 15.4 6.6 ## 6 16.1 7.2 ## 7 16.4 7.2 ## 8 17.2 7.9 ## 9 17.8 7.9 ## 10 17.8 8.2 ## 11 18.5 8.8 ## 12 19.5 9.5 ## 13 19.8 10.4 ## 14 20.0 11.1 ## 15 20.1 10.9 ## 16 20.5 10.7 ## 17 20.9 11.2 ## 18 21.8 11.8 ## 19 22.0 11.3 ## 20 22.2 11.5 ## 21 22.1 11.9 ## 22 22.6 12.2 ## 23 22.9 12.9 ## 24 24.0 13.2 ## 25 24.3 13.6 ## 26 24.6 13.3 ## 27 25.0 14.7 ## 28 25.9 15.4 ## 29 26.1 16.5 ## 30 26.6 15.7 ## 31 27.2 17.0 ## 32 27.6 17.3 ## 33 28.2 17.6 ## 34 28.1 17.6 ## 35 28.4 18.5 ## 36 29.1 18.5 ## 37 29.8 19.6 ## 38 29.9 19.3 ## 39 30.3 19.8 ## 40 31.0 19.9 ## 41 31.3 21.2 ## 42 32.0 21.8 ## 43 32.3 22.2
Is this data tidy?
## state division ## 1 Connecticut (CT) 1 ## 2 Maine (ME) 1 ## 3 Massachusetts (MA) 1 ## 4 New Hampshire (NH) 1 ## 5 Rhode Island (RI) 1 ## 6 Vermont (VT) 1 ## 7 New Jersey (NJ) 2 ## 8 New York (NY) 2 ## 9 Pennsylvania (PA) 2 ## 10 Illinois (IL) 3 ## 11 Indiana (IN) 3 ## 12 Michigan (MI) 3 ## 13 Ohio (OH) 3 ## 14 Wisconsin (WI) 3 ## 15 Iowa (IA) 4 ## 16 Kansas (KS) 4 ## 17 Minnesota (MN) 4 ## 18 Missouri (MO) 4 ## 19 Nebraska (NE) 4 ## 20 North Dakota (ND) 4 ## 21 South Dakota (SD) 4 ## 22 Delaware (DE) 5 ## 23 District of Columbia (DC) 5 ## 24 Florida (FL) 5 ## 25 Georgia (GA) 5 ## 26 Maryland (MD) 5 ## 27 North Carolina (NC) 5 ## 28 South Carolina (SC) 5 ## 29 Virginia (VA) 5 ## 30 West Virginia (WV) 5 ## 31 Alabama (AL) 6 ## 32 Kentucky (KY) 6 ## 33 Mississippi (MS) 6 ## 34 Tennessee (TN) 6 ## 35 Arkansas (AR) 7 ## 36 Louisiana (LA) 7 ## 37 Oklahoma (OK) 7 ## 38 Texas (TX) 7 ## 39 Arizona (AZ) 8 ## 40 Colorado (CO) 8 ## 41 Idaho (ID) 8 ## 42 Montana (MT) 8 ## 43 Nevada (NV) 8 ## 44 New Mexico (NM) 8 ## 45 Utah (UT) 8 ## 46 Wyoming (WY) 8 ## 47 Alaska (AK) 9 ## 48 California (CA) 9 ## 49 Hawaii (HI) 9 ## 50 Oregon (OR) 9 ## 51 Washington (WA) 9
tidyr is a package that reshapes the layout of data frames:
gather(): transforms data from wide to long
spread(): transforms data from long to wide
separate(): splits a single column into multiple columns
unite(): combines multiple columns into a single column
arrange(
summarize(
filter(data, variable == "numeric_value"),
Total = sum(variable)
),
desc(Total)
)
a <- filter(data, variable == "numeric_value") b <- summarise(a, Total = sum(variable)) c <- arrange(b, desc(Total))
data %>%
filter(variable == "value") %>%
summarise(Total = sum(variable)) %>%
arrange(desc(Total))
Code alternatives:
These all produce the same results:
cases %>% gather(Year, n, `2011`:`2013`)
cases %>% gather(Year, n, `2011`, `2012`, `2013`)
cases %>% gather(Year, n, 2:4)
cases %>% gather(Year, n, -Country)
Also note that if you do not supply arguments for na.rm or convert values then the defaults are used
Import the expenditures.csv file in the data folder
Create the following data frame:
long <- expenditures %>% gather(Year, Costs, 2:15)
Code alternatives:
These produce the same results:
storms %>% separate(date, c("year", "month", "day"))
storms %>% separate(date, c("year", "month", "day"), sep = "-")
By default, if no separator is specified, will separate by any regular expression that matches any sequence
of non-alphanumeric values
Import the expenditures.csv file in the data folder
1. Reshape this data from wide to long
2. Separate FY from the Year
HINT: You should end with four variables: 1) State name, 2) A variable of just "FY", 3) Year, 4) Costs
Code alternatives:
These produce the same results:
storms %>% unite(date, year, month, day, sep = "_")
storms %>% unite(date, year, month, day)
If no separator is identified, "_" will automatically be used
Import the facebook.tsv file
dob_year, dob_month & dob_day variables
tidyr is a package that reshapes the layout of data frames:
gather(): transforms data from wide to long
spread(): transforms data from long to wide
separate(): splits a single column into multiple columns
unite(): combines multiple columns into a single column
5 minutes!